Forecasting the Frequency of Changes in Quoted Foreign Exchange Prices with the Autoregressive Conditional Duration Model
Jeffrey R. Russell
University of Chicago - Booth School of Business - Econometrics and Statistics
Robert F. Engle
New York University - Leonard N. Stern School of Business - Department of Economics; Centre for International Finance and Regulation (CIFR); National Bureau of Economic Research (NBER); New York University (NYU) - Department of Finance
This paper applies the Autoregressive Conditional Duration model to Foreign Exchange quotes arriving on Reuter's screens. The Autoregressive Conditional Duration model, proposed in Engle and Russell (1995), is a new statistical model for the analysis of data that do not arrive in equal time intervals. When Dollar/Deutschmark data are examined, it is clear that many of the price quotes carry little information about the price process, as they are simply repeats of the previous quote. By selectively thinning the sample, we develop a measure and forecasts for the intensity of price changes. This measure is related to standard measures of volatility but is formulated in a way that better captures the irregular sampling intervals that are inherent to high frequency financial data. Continuous-stochastic-process theorems for crossing times are used to derive an exact relationship between the intensity of price changes and standard volatility measures. The model might be useful for traders and allows tests that other variables are useful in forecasting the intensity of price changes. Generally, little support is found for price leadership, but other variables influence the intensity of price changes.
JEL Classification: G15working papers series
Date posted: August 22, 1998
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